AI engineered for enterprise scale and trust.
Stalled AI projects get stuck on the same questions: who may see what, where data sits, how you would spot a quality drop, and what it costs at scale.
Outcomes you can expect.
AI readiness & governance
We pick the workflows worth automating and put governance in from the start: who may see what, where data sits, and how any model or agent answer can be explained.
Who it is for
- Teams with an AI pilot that works in the demo and nowhere else.
- Organisations that cannot yet say which workflows are worth automating, or what they would cost.
- Regulated businesses that must show how an AI system decides and who is accountable for it.
What you get
- A shortlist of use cases with the reasons for and against each
- Answers to the data location and access questions, in writing
- A model risk position and the controls that back it
- Logging and lineage that can reconstruct any answer
- Red-team findings with retest evidence
What we do
Choose the use cases
We start from the workflow, not the model, and score candidates on value, data readiness and risk, so the weak ideas die early and cheaply.
Settle the data questions
We establish who is allowed to see what, where the data physically sits, and what may never leave your boundary.
Set the risk position
We write the model risk position your auditor and your own risk team will accept, with the controls behind it.
Record lineage and decisions
Prompts, retrieved sources, tool calls and outputs are logged with the retention you need, so an answer given weeks ago can be reconstructed.
Test the system's defences
We red-team the system for prompt injection, jailbreaks and data leakage, with guardrails in front and behind the model, and fix what we find before anyone relies on it.
AI readiness assessment
A bounded look at which workflows are worth automating and what stands between your pilot and production, ending in a written report and a ranked plan.
What is examined
- Which workflows could repay automation, and which could not, with the reasons
- Whether the data the system needs exists, is clean enough, and may be used for this purpose
- Who is allowed to see what, and whether the pilot respects those rules
- How quality would be measured, and what the system would cost at real volume
How it works
- 01 Scope. We agree which workflows and systems are in, and who we need to talk to.
- 02 Discover. We review the pilot and its data, and talk to the people who would use and own it.
- 03 Assess. We score each candidate on value, data readiness and risk, and test the pilot against real permissions.
- 04 Report. We write up what to build, what to stop and what is missing, in plain language.
- 05 Review. We walk through the report with your team, answer questions and agree what happens next.
What you receive
- A written report with the use cases ranked and the reasons given
- A list of what stands between the pilot and production
- The data, access and risk questions answered in writing
- A readout session with your team
- If you want help, we can build it, or hand the plan to your own team.
- If the answer is not yet, you keep a clear record of why, and of what would change it.
Scope and timing are agreed on a call, before anything is committed.
LLM platforms, retrieval & agents
We build the platform under an AI system: model gateway, RAG pipelines, AI agents and serving, with permissions enforced at retrieval and hard limits on what an agent may do.
Who it is for
- Teams building an assistant over their own documents who need it to respect existing access rules.
- Organisations that want models behind a private boundary, with logging and a way to switch provider.
- Engineering groups giving agents tools and needing those tools safely fenced.
What you get
- A private gateway with logging, residency and access control
- RAG that enforces who may see which document
- AI agents with checkpoints, tracing and boundaries you can read
- Serving and registry infrastructure in your own environment
- A design that lets you change model provider without a rewrite
What we do
Put a gateway in front
One gateway for model access with private endpoints, data residency, access control and logging, so the model becomes a replaceable component.
Build RAG that respects permissions
Ingestion, chunking strategy, embeddings, hybrid search (vector plus keyword) in pgvector or a dedicated store, and reranking, with access rules applied when a result is fetched, not bolted on afterwards.
Orchestrate and fence the agents
Stateful agent workflows in LangGraph or LangChain, with function calling and schema-validated structured outputs, human-in-the-loop checkpoints, tracing of every step, and hard permission boundaries on what each tool may do.
Serve the models
Hosted models through Anthropic, OpenAI, Amazon Bedrock, Azure AI Foundry or Vertex AI, or your own with vLLM on GPUs, using continuous batching, quantization and KV-cache tuning, plus a model registry and capacity planning.
Adapt the model only when it pays
We try prompting, retrieval and few-shot examples first, and fine-tune with LoRA or similar methods only where the evaluation harness shows a gain worth the upkeep.
Keep it portable
Prompts, tools and interfaces stay independent of any one provider or framework, so a change in price or capability does not force a rewrite.
Evaluation, observability & cost
We build the evaluation harness, monitoring and cost controls that tell you whether the system is good, whether it is drifting, and what it costs at ten times the volume.
Who it is for
- Teams who argue about whether a change made things better and settle it by seniority.
- Organisations whose pilot was priced on pilot traffic and have not modelled the real load.
- Anyone who needs to notice a quality drop before customers report it.
What you get
- An evaluation harness and golden dataset your team owns and can extend
- Regression checks that run on every change
- Tracing and drift alerts on live traffic
- Token budgets, caching and cost controls with a ceiling, and a view of where spend goes
- A cost model at expected real volume
What we do
Build the evaluation harness
A golden dataset of real questions with agreed good answers, scored by rules and by LLM-as-judge where that is reliable, so improvement is measured and not argued about.
Add regression suites
Every change to a prompt, model, retriever or agent graph runs through the harness in CI, and a drop in quality blocks the release.
Trace and watch in production
Traces of every model call and agent step, with drift detection and quality scoring on live traffic, and alerts that point at a cause.
Control the spend
Token budgets and context-window management, prompt and prefix caching, semantic caching, model routing and streaming, so cost does not climb in a straight line with usage.
Model the real load
We cost the system in tokens at the volume you expect once everyone uses it, with rate limits and fallbacks for provider outages, before that volume arrives.
Ready to get started?
Book thirty minutes with our team for a clear view of scope and cost.